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arXiv · 1510.04149

Column Selection via Adaptive Sampling

Saurabh Paul·Malik Magdon-Ismail·Petros Drineas
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Abstract

Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm that can be used to improve any relative-error column selection algorithm. Our algorithm delivers a tighter theoretical bound on the approximation error which we also demonstrate empirically using two well known relative-error column subset selection algorithms. Our experimental results on synthetic and real-world data show that our algorithm outperforms non-adaptive sampling as well as prior adaptive sampling approaches.

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Saurabh Paul, Malik Magdon-Ismail, Petros Drineas. 2015-10-14. Column Selection via Adaptive Sampling. https://arxiv.org/abs/1510.04149

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